Project Summary/Abstract The presence of background noise often has a substantial, negative impact on a listener?s ability to understand speech. Unfortunately, deciphering speech in noisy environments is commonplace for listeners. As our recent preliminary data demonstrate, when the speech signal itself is degraded, such as due to the neurological speech disorder of dysarthria, the addition of background noise only further decreases a listener?s ability to understand that speech (Yoho & Borrie, 2018). A highly promising technique developed to remediate much of this speech-in-noise difficulty is termed the Ideal Binary Mask (IBM). The IBM, and IBM estimation, has demonstrated significant improvements in understanding speech in noise for listeners with sensorineural hearing loss. However, research on this technique to date has focused on healthy, intact speech. The proposed research involves an innovative application of the IBM?to overcome the speech-in-noise difficulties with degraded speech. This application is important for listeners with either normal hearing or hearing loss, as even normal hearing listeners struggle considerably to understand degraded speech in noise. This proposal aims to demonstrate proof-of-concept for the application of IBM to a degraded speech signal, starting with the test case of dysarthria. In Aim 1, we quantify the ability of the IBM to restore intelligibility of dysarthric speech in noise to performance levels in quiet. Comparisons will be made between conditions of speech in noise, speech in noise processed by the IBM, and baseline speech in quiet, for both healthy and dysarthric speech. A predictive model will quantify the benefit of IBM processing for normal-hearing listeners, and possible moderators on this benefit. In Aim 2, we examine the benefit achieved from the IBM for listeners with sensorineural hearing impairment. Data analysis will follow that of Aim 1, and model outcomes will be compared to those from Aim 1. Successful completion of this project will inform the development of a R01 proposal evaluating the effects of IBM processing on a wide range of severities and types of degraded or imperfect speech, and account for cognitive processes such as listening effort. Knowledge of how IBM processing works with an imperfect speech signal will support the refinement of current IBM-estimation algorithms, optimizing functionality in realistic listening environments. The possible impact of this proposed research is profound?IBM-based processing has the potential not only to improve the lives of listeners with hearing loss, but also the conversational partners of speakers with dysarthria and other speech disorders. The long-term goal of this work is to provide clinically-significant tools to transform treatment of these two important populations?individuals with speech disorders and individuals with hearing loss. This collaborative research plan is the first proof of concept step to demonstrate several possible novel applications of this promising technique for new clinical treatments.